Dynamic Geometry Capture with a Multi-View Structured-Light System
CALIFORNIA UNIV BERKELEY DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
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Human Motion capture has been an active area of research for many years and has applications in many fields such as gaming, entertainment, physical therapy, and ergonomics. Most commercially available motion capture systems use numerous markers to be placed on the body of the human subject requiring a significant setup time. In this dissertation, we develop the architecture and algorithms for markerless motion capture with a multi-view structured light system. In contrast to existing markerless approaches that use multiple camera streams, we reconstruct the scene by combining the views from three structured light stations using sinusoidal phase shift patterns each equipped with one projector, a stereo pair of cameras for phase unwrapping, and a color camera. The three stations surround the subject and are time multiplexed to avoid interference. Phase-shifted sinusoidal patterns offer low decoding complexity, require as few as three projection frames per reconstruction, and are well suited for capturing dynamic scenes. In these systems depth is reconstructed by determining the phase projected onto each pixel in the camera and establishing correspondences between camera and projector pixels. Typically, multiple periods are projected within the set of sinusoidal patterns, thus requiring phase unwrapping on the phase image before correspondences can be established. There are three novel contributions to this dissertation first, we present a novel phase unwrapping algorithm across space and time in order to generate a temporally consistent point cloud. Specifically, we combine a quality guided phase unwrapping approach with absolute phase estimates from the stereo cameras to solve for the absolute phase of connected regions. Second, we develop a calibration method for multi-camera-projector systems in which sensors face each other as well as share a common viewpoint.